{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print( 'Training data shape: ', X_train.shape)\n",
    "print ('Training labels shape: ', y_train.shape)\n",
    "print ('Test data shape: ', X_test.shape)\n",
    "print ('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print ('Train data shape: ', X_train.shape)\n",
    "print ('Train labels shape: ', y_train.shape)\n",
    "print ('Validation data shape: ', X_val.shape)\n",
    "print ('Validation labels shape: ', y_val.shape)\n",
    "print ('Test data shape: ', X_test.shape)\n",
    "print ('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print ('Training data shape: ', X_train.shape)\n",
    "print ('Validation data shape: ', X_val.shape)\n",
    "print ('Test data shape: ', X_test.shape)\n",
    "print ('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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R8SxwfkQcAWi+ntdfmGY2q4mSPSI+iIhtwIXAdkmXTtqBpF2SViWtrq2tZeM0sxlNdTc+It4B/hHYARyVtBmg+Xqspc3uiFiJiJWlpaUZwzWzrLHJLumzks5qHp8G/BbwI+BJYGfzsp3AE30FaWazm2QgzGZgr6RNjH44PBIRfyfpn4BHJN0MvA7cMFmXbQNhuh04MXBhpQf11d4GHM/Sz9lNHjTXrO2EtJ+osckeEfuByzbY/l/A1ZOGZmbz5b+gM6uEk92sEk52s0o42c0q4WQ3q4RKI8c670x6E/iP5um5wM8G67yd4/gox/FRH7c4fiUiPrvRjkGT/SMdS6sRsTKXzh2H46gwDn+MN6uEk92sEvNM9t1z7Hs9x/FRjuOjPjFxzO13djMblj/Gm1ViLskuaYekf5P0qqS5zV0n6ZCklyW9KGl1wH73SDom6cC6bedIelrSj5uvZ88pjjsl/WdzTl6UdM0AcWyR9ANJByW9Iun3m+2DnpNCHIOeE0mfkfTPkl5q4vjjZvts5yMiBv0HbAJ+AnwBOBV4Cbhk6DiaWA4B586h3y8BlwMH1m37U+C25vFtwJ/MKY47gT8Y+HxsBi5vHp8B/DtwydDnpBDHoOeE0TjV05vHpwDPAlfMej7mcWXfDrwaEa9FxC+A7zGavLIaEfEM8NZJmwefwLMljsFFxJGIeKF5/B5wELiAgc9JIY5BxUjnk7zOI9kvAH667vlh5nBCGwF8X9LzknbNKYYPLdIEnrdI2t98zO/914n1JG1lNH/CXCc1PSkOGPic9DHJ6zySfaOpNOZVErgyIi4Hfgf4pqQvzSmORXIfcBGjNQKOAHcP1bGk04FHgVsj4t2h+p0gjsHPScwwyWubeST7YWDLuucXAm/MIQ4i4o3m6zHgcUa/YszLRBN49i0ijjZvtBPA/Qx0TiSdwijBHoyIx5rNg5+TjeKY1zlp+p56ktc280j254CLJX1e0qnAVxlNXjkoScuSzvjwMfBl4EC5Va8WYgLPD99MjesZ4JxoNJngA8DBiLhn3a5Bz0lbHEOfk94meR3qDuNJdxuvYXSn8yfAH84phi8wqgS8BLwyZBzAQ4w+Dv4vo086NwO/zGgZrR83X8+ZUxx/DbwM7G/eXJsHiOPXGf0qtx94sfl3zdDnpBDHoOcE+DXgX5r+DgB/1Gyf6Xz4L+jMKuG/oDOrhJPdrBJOdrNKONnNKuFkN6uEk92sEk52s0o42c0q8X/HP4TBuHmAHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print (mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print (X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.319717\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "print ('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -28.331552 analytic: -28.331552, relative error: 7.408751e-12\n",
      "numerical: 12.955129 analytic: 12.955129, relative error: 1.191100e-11\n",
      "numerical: -7.648356 analytic: -7.648356, relative error: 3.405520e-11\n",
      "numerical: 2.729686 analytic: 2.729686, relative error: 1.237573e-11\n",
      "numerical: -3.176685 analytic: -3.176685, relative error: 9.714133e-11\n",
      "numerical: 8.020618 analytic: 8.009569, relative error: 6.892623e-04\n",
      "numerical: -1.259417 analytic: -1.259417, relative error: 1.901475e-10\n",
      "numerical: 26.748354 analytic: 26.748354, relative error: 1.314054e-12\n",
      "numerical: 6.812133 analytic: 6.820706, relative error: 6.288359e-04\n",
      "numerical: 13.504296 analytic: 13.504296, relative error: 2.155167e-11\n",
      "numerical: 53.812996 analytic: 53.830202, relative error: 1.598471e-04\n",
      "numerical: 10.305999 analytic: 10.305999, relative error: 1.106216e-12\n",
      "numerical: 8.273940 analytic: 8.273940, relative error: 4.481727e-11\n",
      "numerical: 21.109038 analytic: 21.174876, relative error: 1.557051e-03\n",
      "numerical: -1.698371 analytic: -1.698371, relative error: 1.332408e-10\n",
      "numerical: -40.295545 analytic: -40.295545, relative error: 9.404711e-13\n",
      "numerical: -8.280501 analytic: -8.317937, relative error: 2.255391e-03\n",
      "numerical: -28.102841 analytic: -28.102841, relative error: 2.349547e-12\n",
      "numerical: 3.422546 analytic: 3.422546, relative error: 2.039543e-10\n",
      "numerical: 12.443852 analytic: 12.443852, relative error: 6.295407e-12\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 1e2)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 1e2)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** *fill this in.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.319717e+00 computed in 0.155039s\n",
      "Vectorized loss: 9.319717e+00 computed in 0.005065s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print ('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print ('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print ('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.146494s\n",
      "Vectorized loss and gradient: computed in 0.005042s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print ('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print ('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print ('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 796.304350\n",
      "iteration 100 / 1500: loss 288.878240\n",
      "iteration 200 / 1500: loss 108.177292\n",
      "iteration 300 / 1500: loss 42.738716\n",
      "iteration 400 / 1500: loss 18.484407\n",
      "iteration 500 / 1500: loss 10.334065\n",
      "iteration 600 / 1500: loss 7.053013\n",
      "iteration 700 / 1500: loss 6.110896\n",
      "iteration 800 / 1500: loss 5.629576\n",
      "iteration 900 / 1500: loss 5.139512\n",
      "iteration 1000 / 1500: loss 4.796175\n",
      "iteration 1100 / 1500: loss 4.966433\n",
      "iteration 1200 / 1500: loss 5.676842\n",
      "iteration 1300 / 1500: loss 5.665403\n",
      "iteration 1400 / 1500: loss 5.637974\n",
      "That took 10.539915s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print ('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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COG0pSZLOaSFGyN6RUroypbSj+PwW4L6U0lbgvuJzImIbcCOwHXgv8MmIyC9AfSXxQiA7aCCTJEnnkMWU5fXAHcXjO4AbprXfmVIaSSntB/YBV2dQ37xorK6kqabSKUtJknROpQ5kCfh6RDwcER8ttq1MKR0DKD52FNvXAoemvfdwsW3JWt9aw6HT7tYvSZJeWUWJP/9NKaWjEdEB3BsRu1/h3JihLf3QSVPB7qMAGzZsmJ8qS2RDay27j7s5rCRJemUlHSFLKR0tPnYCX2RqCvJERKwGKD52Fk8/DKyf9vZ1wNEZPvP2lNKOlNKO9vb2UpZ/wda31HL49BCTkz+UKyVJkl5UskAWEXUR0fDCMfAe4CngbuCm4mk3AV8qHt8N3BgRhYjYDGwFHixVfQthXWstoxOTdPaNZF2KJElaxEo5ZbkS+GJEvPDnfDaldE9EPATcFREfAQ4CHwRIKe2KiLuAp4Fx4OaU0kQJ6yu5DS9sfXFmkFVN1RlXI0mSFquSBbKU0nPAa2ZoPwVce5b33AbcVqqaFtr6luLmsKcHef2m1oyrkSRJi5U79ZfQ2pYaItyLTJIkvTIDWQkVKvKsbKh26wtJkvSKDGQltqG11s1hJUnSKzKQldi61hrvZylJkl6RgazENrTWcrx3mOGxJX3BqCRJKiEDWYltbqsjJRwlkyRJZ2UgK7HNbXUAPHdyIONKJEnSYmUgK7FNxUC230AmSZLOwkBWYo3VlbTVF9jfZSCTJEkzM5AtgM1ttY6QSZKkszKQLYDNbXXsP2UgkyRJMzOQLYDNbfV09Y3QNzyWdSmSJGkRMpAtgBeutHz+pFtfSJKkH2YgWwBb2l/Y+qI/40okSdJiZCBbABtaa4lw6wtJkjQzA9kCqK7Ms7a5xkAmSZJmZCBbIJvb6gxkkiRpRgayBbKlrY79XQOklLIuRZIkLTIGsgWyua2OvpFxTvaPZl2KJElaZAxkC2Rzez3gwn5JkvTDzhnIImJlRHwqIr5afL4tIj5S+tKWly0v3mTcrS8kSdJLzWaE7C+ArwFris+/D3y8VAUtV2uaa6jK53jOETJJkvQyswlkbSmlu4BJgJTSODBR0qqWoXwu2LCilv1dBjJJkvRSswlkAxGxAkgAEfFGoKekVS1Tbn0hSZJmUjGLc34VuBu4KCL+AWgHfqKkVS1TW9rq+NaeLiYmE/lcZF2OJElaJM4ZyFJKj0TE24BLgQD2pJTGSl7ZMrS5rY7RiUmOdg+xvrU263IkSdIicc5AFhH/+8uaXhsRpJQ+XaKalq3NbS/cZHzAQCZJkl40mynL1087rgauBR4BDGTnaUtxL7K9J/p42yXtGVcjSZIWi9lMWX5s+vOIaAL+R8kqWsbaGwp0NBR4+mhv1qVIkqRFZC479Q8CW+e7kHLx6rVNPHXUi1QlSdIPzGYN2d9S3PKCqQC3DbirlEUtZ9vXNvGNPZ0MjU5QU5XPuhxJkrQIzGYN2e9OOx4HDqSUDpeonmVvS1sdkwmO9gxxUXFNmSRJKm+zWUP2rYUopFx0NBYAONEzbCCTJEnAKwSyiOjjB1OVL3kJSCmlxpJVtYytaqwG4HjvcMaVSJKkxeKsgSyl1LCQhZSLNc01VOSCfZ39WZciSZIWidmsIQMgIjqY2ocMgJTSwZJUtMxVV+bZtqaRRw92Z12KJElaJM657UVEvD8i9gL7gW8BzwNfLXFdy9qV65t54nA3E5MzzQhLkqRyM5t9yP4D8Ebg+ymlzUzt1P8PJa1qmXvV2iYGRic4eHow61IkSdIiMJtANpZSOgXkIiKXUvoGcGWJ61rWNhbvY3nIQCZJkpjdGrLuiKgHvg18JiI6mdqPTHO0YUUxkJ0xkEmSpNmNkF3P1O2SfgW4B3gW+N9KWdRyt7Khmqp8zilLSZIEzC6QfRRYk1IaTyndkVL6w+IU5qxERD4iHo2ILxeft0bEvRGxt/jYMu3cWyNiX0TsiYjrzv/rLA25XLCupYbDp4eyLkWSJC0CswlkjcDXIuI7EXFzRKw8zz/jl4Fnpj2/BbgvpbQVuK/4nIjYBtwIbAfeC3wyIpbtzR43tdW5F5kkSQJmEchSSr+ZUtoO3AysAb4VEX8/mw+PiHXAjwF/Oq35euCO4vEdwA3T2u9MKY2klPYD+4CrZ/UtlqBXr21ib2cfAyMux5MkqdzNZoTsBZ3AceAU0DHL9/w+8K+ByWltK1NKxwCKjy981lrg0LTzDhfblqVtaxqZTLDXUTJJksrebDaG/ZcR8U2mphfbgJ9PKV0xi/e9D+hMKT08y1pihrYf2jk1Ij4aETsjYmdXV9csP3rxubhj6sbizxrIJEkqe7PZ9mIj8PGU0mPn+dlvAt4fET/K1C2XGiPiL4ETEbE6pXQsIlYzNfIGUyNi66e9fx1w9OUfmlK6HbgdYMeOHUt2q/sNrbVU5IJnuwxkkiSVu9msIbtlDmGMlNKtKaV1KaVNTC3W/18ppZ8F7gZuKp52E/Cl4vHdwI0RUYiIzcBW4MHz/XOXisp8jo0rag1kkiRp9jcXn0efAO6KiI8AB4EPAqSUdkXEXcDTTG08e3NKaSKD+hbMRe31PNs1kHUZkiQpYwsSyFJK3wS+WTw+xdT9MGc67zbgtoWoaTG4qKOeb+zpZGxiksr8+VxfIUmSlpPZLOqvi4hc8fiSiHh/RFSWvrTl7+L2esYmEgdOOUomSVI5m82wzLeB6ohYy9SVlj8H/EUpiyoXl69uBGDX0d6MK5EkSVmaTSCLlNIg8OPAf00pfQDYVtqyysPWlfVU5XM8bSCTJKmszSqQRcQ1wM8Af1dsy+JigGWnMp/jklX1jpBJklTmZhPIPg7cCnyxeCXkFuAbpS2rfGxf3cSuoz2ktGS3VJMkSRdoNvuQfSul9P6U0n8qLu4/mVL6pQWorSxsX9vImcExjvUMZ12KJEnKyGyusvxsRDRGRB1Te4TtiYh/VfrSysM2F/ZLklT2ZjNluS2l1AvcAHwF2AB8uKRVlZHLVzcSgQv7JUkqY7MJZJXFfcduAL6UUhpjhpt+a27qChVsbK1l93EDmSRJ5Wo2gexPgOeBOuDbEbERMD3Mo8tXN/LMMbtUkqRyNZtF/X+YUlqbUvrRNOUA8I4FqK1sXL66kQOnBxkYGc+6FEmSlIHZLOpvioj/EhE7iz//manRMs2TV61tJCV44nBP1qVIkqQMzGbK8s+APuAniz+9wJ+Xsqhy87qNrUTAQ8+fzroUSZKUgdnsuH9RSumfTnv+mxHxWKkKKkdNNZVsbK11HZkkSWVqNiNkQxHx5heeRMSbgKHSlVSeXNgvSVL5ms0I2S8An46IpuLzM8BNpSupPF22qpF7dh1nYGScuoK3CpUkqZzM5irLx1NKrwGuAK5IKV0FvLPklZWZy1c3kBLsOdGXdSmSJGmBzWbKEoCUUm9xx36AXy1RPWXr8uItlJy2lCSp/Mw6kL1MzGsVYl1LDfWFCnYfc4RMkqRyM9dA5q2T5llEcNmqBkfIJEkqQ2ddPR4RfcwcvAKoKVlFZezy1Y188dEjpJSIcBBSkqRycdYRspRSQ0qpcYafhpSSlwGWwGWrG+gfGefg6cGsS5EkSQtorlOWKoE3bG4F4Fvf78q4EkmStJAMZIvIxR0NtNVXseuI68gkSSonBrJFZtOKOvafGsi6DEmStIAMZIvM1pUNPHO0l9HxyaxLkSRJC8RAtsi8/dJ2+kbGefJId9alSJKkBWIgW2QuWdkAwHNdTltKklQuDGSLzLqWGipywbMGMkmSyoaBbJGpzOe4bHWDU5aSJJURA9kidNX6Fh4/1MPEpHeokiSpHBjIFqEr1zfTPzLOvs7+rEuRJEkLwEC2CF21oRmARw+eybgSSZK0EAxki9Dmtjqaaip57JDryCRJKgcGskUoIrhqQzOPHjSQSZJUDgxki9RV61v4fmcffcNjWZciSZJKzEC2SF25oZmU4InDPVmXIkmSSsxAtkhduW5qYb/ryCRJWv4MZItUU20lF7XXeaWlJEllwEC2iF21oYVHD3aTkhvESpK0nBnIFrEr1zdzamCUQ6eHsi5FkiSVUMkCWURUR8SDEfF4ROyKiN8strdGxL0Rsbf42DLtPbdGxL6I2BMR15WqtqXixQ1iDzltKUnSclbKEbIR4J0ppdcAVwLvjYg3ArcA96WUtgL3FZ8TEduAG4HtwHuBT0ZEvoT1LXqXrmygpjLvfmSSJC1zJQtkacoLN2OsLP4k4HrgjmL7HcANxePrgTtTSiMppf3APuDqUtW3FFTkc1yxrolHvdJSkqRlraRryCIiHxGPAZ3AvSmlB4CVKaVjAMXHjuLpa4FD095+uNj28s/8aETsjIidXV1dpSx/UbhqQwtPH+1heGwi61IkSVKJlDSQpZQmUkpXAuuAqyPiVa9wesz0ETN85u0ppR0ppR3t7e3zVeqideX6ZsYmEruO9mZdiiRJKpEFucoypdQNfJOptWEnImI1QPGxs3jaYWD9tLetA44uRH2L2YsL+92PTJKkZauUV1m2R0Rz8bgGeBewG7gbuKl42k3Al4rHdwM3RkQhIjYDW4EHS1XfUrGysZq1zTXu2C9J0jJWUcLPXg3cUbxSMgfclVL6ckTcD9wVER8BDgIfBEgp7YqIu4CngXHg5pSSC6eYuq+lV1pKkrR8lSyQpZSeAK6aof0UcO1Z3nMbcFupalqqrlrfzN89cYzO3mE6GquzLkeSJM0zd+pfAn6wQayjZJIkLUcGsiVg+5omKvPhtKUkScuUgWwJqK7Ms211I495CyVJkpYlA9kScdWGFh471M3g6HjWpUiSpHlmIFsirtu+iuGxSe59+kTWpUiSpHlmIFsi3rC5lY6GgoFMkqRlyEC2RORywWs3tPDUkZ6sS5EkSfPMQLaEvGptI8+fGqR3eCzrUiRJ0jwykC0hr1rbBOD2F5IkLTMGsiXk6s2tNNdW8tcPH866FEmSNI8MZEtIbVUF12xZweOHHSGTJGk5MZAtMa/d0MKBU4McPjOYdSmSJGmeGMiWmLdf2g7A9549lXElkiRpvhjIlpgt7fVUV+bYfawv61IkSdI8MZAtMflc8Oq1TTz0/OmsS5EkSfPEQLYEvf3SDp480sOJ3uGsS5EkSfPAQLYEvfOyDgC+s/dkxpVIkqT5YCBbgi5d2UBDoYL7XdgvSdKyYCBbgnK54EdfvZq/eewIJ/tHsi5HkiRdIAPZEvXhazYyMZn4zt6urEuRJEkXyEC2RF22qoGqvNtfSJK0HBjIlqiKfI6tK+u9jZIkScuAgWwJe/PWNnY+f4be4bGsS5EkSRfAQLaEXXvZSsYnE//g9heSJC1pBrIl7KoNzdRU5vnH59z+QpKkpcxAtoRV5nNcc9EKvrbrBBOTKetyJEnSHBnIlrgPXLWW473DPHLwTNalSJKkOTKQLXFvv7SdqnyOrz11POtSJEnSHBnIlriG6kp2bGrhgf2nsy5FkiTNkYFsGdi+ppE9J/oYHpvIuhRJkjQHBrJl4F2Xr2R0fJL/+dChrEuRJElzYCBbBt6wZQWXrKzna7tcRyZJ0lJkIFsm3nxxOw8fOOO0pSRJS5CBbJl497aVjIxPcvfjR7MuRZIknScD2TLxxi2trGup4R63v5AkackxkC0TEcF7tq3iu/tOMjAynnU5kiTpPBjIlpH3bJ+62vLb3+/KuhRJknQeDGTLyI6NLbTUVvL1p09kXYokSToPBrJlpCKf452XreS+Z04wNjGZdTmSJGmWDGTLzHu2r6R3eJwHvZWSJElLhoFsmXnr1naqK3Pc67SlJElLRskCWUSsj4hvRMQzEbErIn652N4aEfdGxN7iY8u099waEfsiYk9EXFeq2pazmqo8b9naztd3HSellHU5kiRpFko5QjYO/FpK6XLgjcDNEbENuAW4L6W0Fbiv+JziazcC24H3Ap+MiHwJ61u23rNtJUd7htl1tDfrUiRJ0iyULJCllI6llB4pHvcBzwBrgeuBO4qn3QHcUDy+HrgzpTSSUtoP7AOuLlV9y9m1l68kF/B1720pSdKSsCBryCJiE3AV8ACwMqV0DKZCG9BRPG0tcGja2w4X217+WR+NiJ0RsbOry/22ZtJaV8XrN7XyxbPZ9eoAAB86SURBVMeOMDLuvS0lSVrsSh7IIqIe+Gvg4ymlV5pDixnafmgRVErp9pTSjpTSjvb29vkqc9n5P99xMYdOD3HXzsNZlyJJks6hpIEsIiqZCmOfSSl9odh8IiJWF19fDXQW2w8D66e9fR3gnbLn6G2XtLO1o56vPnks61IkSdI5lPIqywA+BTyTUvov0166G7ipeHwT8KVp7TdGRCEiNgNbgQdLVV85uPbylTy4/zS9w2NZlyJJkl5BKUfI3gR8GHhnRDxW/PlR4BPAuyNiL/Du4nNSSruAu4CngXuAm1NKLoC6AO+6vIPxyeS9LSVJWuQqSvXBKaXvMvO6MIBrz/Ke24DbSlVTublqw9S9Le97ppP3XbEm63IkSdJZuFP/MpbPBe+4tINv7Olk3HtbSpK0aBnIlrn3bF9F9+AYf/+Mt1KSJGmxMpAtc+/etpK1zTX89tf2MDzmkjxJkhYjA9kyl88F/8fbtvBc1wBfcQsMSZIWJQNZGfjZN2xkbXMNX3rMbd0kSVqMDGRlIJcLrr9yDd/dd5KT/SNZlyNJkl7GQFYmrr9yLROTib993FEySZIWGwNZmbh0VQOvWd/MZx44mHUpkiTpZQxkZeTHXr2KfZ39HDo9mHUpkiRpGgNZGXn3tlVU5XP8ybefzboUSZI0jYGsjGxuq+Pd21bypceO0tXn4n5JkhYLA1mZ+di1F9M3PM7fPHok61IkSVKRgazMXLaqkdesa+KvHznM5GTKuhxJkoSBrCx9+JpN7D7ex3f2ncy6FEmShIGsLL3vitVUVeT4/MOHsy5FkiRhICtL1ZV5/vmbNvO3jx9l9/HerMuRJKnsGcjK1D9/8yYAvvCIi/slScqagaxMdTRU85atbXzqu/vZf3Ig63IkSSprBrIy9ts/cQUTk4kvPuJaMkmSsmQgK2Orm2q4elMrt3/nOXoGx7IuR5KksmUgK3O//v5tjIxP8vv3fT/rUiRJKlsGsjK3fU0T117Wwdd3nSAlN4qVJCkLBjLxY1es5kj3EPc+fSLrUiRJKksGMvG+K9awvrWGW7/wJMNjE1mXI0lS2TGQicp8jl9/33ZODYzynb3eTkmSpIVmIBMAb7u0naaaSn7trsfoHfaKS0mSFpKBTMDUKNl//uBr6B0e566HDmVdjiRJZcVAphe9a9tKXrOuif/uvmSSJC0oA5le4rdueDUnekf4ywcOZF2KJEllw0Cml3j1uibesLmVT9//vGvJJElaIAYy/ZBfePtFnOgd4b983d37JUlaCAYy/ZB3XNrBh3as57MPHuRYz1DW5UiStOwZyDSjX3znxaSU+K//a1/WpUiStOwZyDSj9a21/MwbNvK5Bw/y7e93ZV2OJEnLmoFMZ/Wr77mEdS01/Lu/eYrJSW88LklSqRjIdFaN1ZX86+su4+DpQT7/yOGsy5EkadkykOkVve+K1VzcUc9//voeDp0ezLocSZKWJQOZXlFE8Fs3vIoTvSP89tf2ZF2OJEnLkoFM5/TGLSv4l2+/iL99/Cj3PHUs63IkSVp2DGSalV951yW8am0j/+HLzzDhAn9JkuaVgUyzUlWR4xffcTFHuof483/Yn3U5kiQtKyULZBHxZxHRGRFPTWtrjYh7I2Jv8bFl2mu3RsS+iNgTEdeVqi7N3XXbV/Guy1fy2/fs4YnD3VmXI0nSslHKEbK/AN77srZbgPtSSluB+4rPiYhtwI3A9uJ7PhkR+RLWpjmICH7nJ66grb6KX/mfjzE8NpF1SZIkLQslC2QppW8Dp1/WfD1wR/H4DuCGae13ppRGUkr7gX3A1aWqTXPXUlfFr79/O892DXDrF57MuhxJkpaFhV5DtjKldAyg+NhRbF8LHJp23uFi2w+JiI9GxM6I2NnV5S19snDd9lV87J0X88VHj/D1XcezLkeSpCVvsSzqjxnaZryUL6V0e0ppR0ppR3t7e4nL0tl87J1b2ba6kX/zxSc52T+SdTmSJC1pCx3ITkTEaoDiY2ex/TCwftp564CjC1ybzkNVRY7fvH47ZwbH+NhnH/Vel5IkXYCFDmR3AzcVj28CvjSt/caIKETEZmAr8OAC16bz9PpNrfyH61/F/c+d4jMPHMi6HEmSlqxSbnvxOeB+4NKIOBwRHwE+Abw7IvYC7y4+J6W0C7gLeBq4B7g5peQlfEvAT129nrdsbePff2kXv/O13VmXI0nSkhQpLd2pph07dqSdO3dmXUbZO9E7zE1/9iC7j/fxtY+/lUtXNWRdkiRJi05EPJxS2jHTa4tlUb+WsJWN1Xzu599IoSLHjbffz/MnB7IuSZKkJcVApnnRUlfFH/7UVZwZHOPff+kpRsadcZYkabYMZJo3121fxW0feBXf2XuSn/zj+1nK0+GSJC0kA5nm1c+8YSO/9u5LePxwD7d/+7msy5EkaUkwkGnefeQtm7l6Uyv/8au73clfkqRZMJBp3tVWVfCX/+INbFvdyL/6/BM8fOBM1iVJkrSoGchUElUVOf74Z19HQ3UFH/zj7/HQ8y+/z7wkSXqBgUwls2FFLX/y4dfR0VDNz/35QzxxuDvrkiRJWpQMZCqp7Wua+OLNP0JTTSUf+OT3vMWSJEkzMJCp5FY31fBXv3ANG1fU8m+/+BR/+Y+GMkmSpjOQaUGsaa7hyx97M2+9pJ1/9zdPceeDB7MuSZKkRcNApgVTW1XBp27awVsvaefffPFJfu/e77t5rCRJGMi0wCrzOf7bT1/Fm7e28wf37eXn/uIhDp8ZzLosSZIyZSDTgmuoruSPf/a1XLm+mW/u6eKX73yMnqGxrMuSJCkzBjJloraqgr+5+U38/oeu5OEDZ3jP732Le58+kXVZkiRlwkCmTN1w1Vr+4udeTxD8/Kd38sffejbrkiRJWnAGMmXu7Zd28He/9GYKFTk+8dXd3Hj7/U5hSpLKioFMi8KK+gJP/MZ7+Hc/djk7nz/DT/zR97j78aNehSlJKgsGMi0ahYo8/+ItW/idD17Bs139/NLnHuXX7nqc4bGJrEuTJKmkDGRadD5w1Tq+d8u1vGptI1949AiX/ft7+O7ek1mXJUlSyRjItCitaqrmMx95I//vB14NwM9/eif//dvP0TM4xuSk05iSpOXFQKZFq6m2kp9+wwa+d8s7ed3GFm77yjO85v/5Or/1d89kXZokSfPKQKZFb01zDf/jI1fz//30VQD82T/s50N/cj9fffKYi/4lSctCLOX/oO3YsSPt3Lkz6zK0gPqGx/j0/Qf4zD8e4GjPMG+/tJ0fe/VqfuJ164iIrMuTJOmsIuLhlNKOGV8zkGkp6h8Z57e+/DR3PnQIgGu2rOAPbrySjsbqjCuTJGlmBjItWz2DY3zyW/v41Hf2Mz6ZaKqp5CNv3sxH37qF6sp81uVJkvQiA5mWvf0nB/jdr+3h7548BkBdVZ4PX7OJX3vPJVTmXSopScqegUxl5d6nT/Dzn576e1FXlWdgdILf+9BruOHKta4zkyRlxkCmstMzNMZ9z5zg7seP8s09XQBsX9PIT79hAz+5Y72jZpKkBWcgU1l75OAZfvyT36MiF4wXN5W9elMrb7q4jV9858Xkc46aSZJKz0AmAROTib9/5gR3fO95HjvUzeDoBPlc8IbNrfyzH9lEU00ll65qoLm2KutSJUnL0CsFsoqFLkbKSj4XXLd9FddtX8Wp/hHufOgQjxw4wzf2dPK9Z08B0FZfxXu2r+JHLlrBmy9uM5xJkhaEI2Qqe8NjEzx+qJtPfXc/Ow+c4fTA6Iuv7djYwpb2OravaeJDr1/vVhqSpDlzylI6D6cHRvn8w4d45EA3z58aYPfxvpe8/q7LV3Ld9pX8+GvXkQu8clOSNCsGMukC9I+M85Unj/Hp+5/nqSO9L7a/kMOu2bKC+kIFb9iygtduaGZDay2VFTkaqyuzKViStCgZyKR5MjAyzpnBUb6xe2rd2f/a3cnI+ORZz//wGzfypotX0FpX4PWbWogIRscnyQVUuPWGJJUVA5lUQsNjE4yMTfLkkR7u2XWMXUd7efRg9w+d11JbydhEon9kHICfuno9Vfkcb7q4jYs76mmsqSSAFfWFBf4GkqSFYCCTMpBSorNvhO+f6OPRg93sPt7LE4d7OHxm6BXfV1eV59rLV9JaV8XGFbVU5nM8sP80P/X69Vxz0QoGRyeoK1SQUnL9miQtIQYyaREZm5jk4OlBGqsref7UAD2DY+w50cc9Tx3nySM9wNT6tNn+01zbXMNFHfVsbK3lzOAo11y0gp3Pn+H7J/p497aVXL66kUtWNjA2McnWjvoXA50kaWEZyKQl6Gj3EENjE5zoGQbggf2nef7UAEOjE3QPjvHYoW5GJ6bWr7U3FOjqG5n1Z9cXKmitq6Krb4RrLlrBqYFRTvQMc+X6Zta31tA7NM7zpwZ488Vt7D7Rx7bVjVTlc2xpr6N/ZJxrtqxgz4k+Tg+M8uq1TWxuq2NkfJL+kXFqq/JMTCYaqisZHZ+kb3iMhuIFDlUVOUf2JJUtA5m0jKWUGJtIHDozyKn+UaoqcpzqH+HAqUFO9A7TWFPJ0OgEpwZG6B+ZoKW2khO9wzzXNcDezn6aairJ5+LF/ddyAZPn+WuhKp97MRxON/12VQANhQr6imvoLlvVQGN1JY01lVTkglMDI3T2jbChtZZCRY6+4XGaaqZqfeJID2+7pJ1v7uniLVvbGBmbZGB0nFWN1bxmfTNHu4foHhxj25pGRsYn6BseZ2IycdmqBh491M2lKxuIgHueOs6r1jbR2TvCprY6cgHjk4m/2nmIm99xMSPjkwyOjtNcU0VdoYIV9VXFADzKRR311Bcq2NvZT9/wOM929bN5RR2b2+o4PThKW30VJ/tGiYBHD3VzUXs9PUNjrKiroqOhwJrmGp440sOKuqnP7h6c6u9LVzUwMDLBQ8+f5lVrmjjRO8zKxmoaqit45lgv/SPjNNZU0lpbxdDYBJX5YGISJlNiZWM145OTnOwfpammko6GAqcHRjk9MMqa5momJuHB/afoH5ngn/3IJgoVObqHxhganeDAqQE6GqvJxdS9X/tHxhkdn6RnaIy3bG1n19Ee1rfW0lJbRWfvMLVVFVRV5DjRO0wErKgrcKR7iFxMfYeUoLWuilP9oxw4PcDqphomU3qxD6oqchw4NUhz7dTft8lJeO5kP621VS9e4FJTmSdi6nMeP9TNgdODvHVrO2uaqznSPURLbRUTk1Of2T04xsrGAlUVOf764cO87dJ2ChV5zgyOUpHL0dk3THNtFRd31JOPYGhsgqPdQ6xuqmZkfJL6QgU9Q2OsbKxmMiXODI7yyIFurlzfTP/IOFUVOcYmJqmtytNWX+CxQ90EsKa5hsp8jp0HTrOyoZo3b23jcPHf3gt/Z3qGxujqG2F4bJIzA6PUVOWpK+SZmISLO+p5/uQAFfmgMp+jrb5AoSJHfaGCJ470cKJ3mI6GAg3VlaxsLLD3RD+9w2NsaaunUJljaHSCukKe6so8FbkcR7qHGBmbYH1rLVUVOfK54MkjPRw6PcjF7VPvaaqpYl1LDYOjExw+M0jP0BgTk4mh0QlyuSClxJHuYba013H5qkaeOtLDyf4RXrO+maaaSvZ29lNfyBMRjE9M9dXWjnomi797dh/vpaYyT3tDgZrKCnYf7+V9V6yhe2iUwZEJGmsqqa3K09U3wqmBUZ7r6qcin2NlQ4Gu/hEq8zkuaq8nAgIYnZikpjJP9+AY7Q0FjvUMMZmm1uFOJth9vI9NK2pZ31LLqYGp33UN1ZW0NxT4zt4uLm6v57UbW4p3ZBnnkpUNDI9N8lxXP9dctIJdR3vpHx5n44paBkcn2HngDGubq2mrL7BjU+v5/xI+D0sqkEXEe4E/APLAn6aUPnG2cw1k0vyZmExMpkQ+guHxCZ451kddIc+jB7vZ0FpLRS6YTHC8d4jP/ONBJlPiQ69fz77OfiKCmso8jx3qZm1LDbuP9XJ6YJTLVjXSXFvJ+GRi74k++kbGaShUsP/kABtX1DE0NkEAQ2NTIapnaIyGQgUEjIxPMvoKV7CeTUUuqK+uYGRskqGxifnvKEnL0o6NLfzVL1xT0hH8JXPrpIjIA/8NeDdwGHgoIu5OKT2dbWXS8pfPBXmmfhHVVlXwuo0tAFy2qvGHzv3AVetKUkP/yDj109a3jY5PvjjNCVMjORX5HOMTU+39w+NEBBHwyIEzbF/bxOrGanK54GT/CPc/e4q3XtJOZ+8w1cX/x72xrZaUpq6OHZ9MHO8ZIhdBW32B2qo8+zr7OXRmiK0d9XT2jdBaV0llPsfg6AS9Q2M8f2qAN13cxlNHeugZGqOtvsDqphryuaC1bupK2o6GAvc/d4pHDnTzodevZ3R8kqeP9dDZO8LFHVOjFi8E0OGxCRqqK+kZGqOmMv/iVbiNNZWMjE2w53gfV25oZnVTNXVVFZwZHOUrTx7nzVvbXhxFbKufmn7uGRrjZP8oFbmgqaaS0YlJ6qryPH2slxX1heKIZAW7j/VRqMjR0Vjgu/tO8eNXrWV0fJJCZY7dx/tY1VjN4OgE7Q0FDp4a4JJVDfQMjXGqf5RDpwfZ3F7Hse5hVjdXc+j0IBOTiaaaSnYf76Orb4Rtaxp52yXt9A6Ncbx3asr99MAYWzvqaWso0DM4ysTk1BXHK4ojRLkIaqvydA+NcbxnmIeeP837X7OGqooce0/001pXRT4XVFfmqK2qIAKO9wwzPpk4MzDKupYavvX9LgqVed5xaQc1lXn+7smjXLaqkYAX/95MJtjUVkv/yDi5CI73DNPZN8yTR3q4dGUjF3XU0VBdydDoOFX5HGMTid7hMVY31dBSW0n30BhPHO6mobqS1U3VnBkYZffxPi4r3gd3bGKSzr6RF0du6wsVrGupYXhsguGxyRf7Z/fxPgZGxlnbXMOZwVG6B8dYUV/F+ESiqXbqf9N9nf08cqCb1c3VbF/TRPfgKPs6+7m4o56qihwPPHeakfEJmmuraKqpZHB0gnUtNWxaUccTh7tZUV/FZJpaj9o9NEpHQzVPH+2lpipHRS7Haze2cKp/hOrKPGMTk6QEB04NsqK+iobqCsYmEs919fPGLSvI54KR8QlO9Y/SWjd1S7mBkXEOnxli+9omvrmnk7b6wosj7qPjk+RzQUdDgZ6hMQ6cGqQiP/X75Z2XdTAyNslw8fP2dvaxaUUdn33gIP/0detY01zNqf5RJlPi4OlBNrbWsbalhs7eYeoKFew62ktTTSU1VXkOnR6kvaFA9+AY61pq6B4co7WuipP9I0xMJhprKunqG6EiFxQqp0ZiK3I5NrTWsutoL+0NBYZGx2lrKHD9lWszXU6xqEbIIuIa4DdSStcVn98KkFL6jzOd7wiZJElaKl5phGyx7Uy5Fjg07fnhYtuLIuKjEbEzInZ2dXUtaHGSJEmlsNgC2UxjhS8Zwksp3Z5S2pFS2tHe3r5AZUmSJJXOYgtkh4H1056vA45mVIskSdKCWGyB7CFga0Rsjogq4Ebg7oxrkiRJKqlFdZVlSmk8In4R+BpT2178WUppV8ZlSZIkldSiCmQAKaWvAF/Jug5JkqSFstimLCVJksqOgUySJCljBjJJkqSMGcgkSZIyZiCTJEnKmIFMkiQpYwYySZKkjBnIJEmSMmYgkyRJypiBTJIkKWMGMkmSpIwZyCRJkjIWKaWsa5iziOgCDizAH9UGnFyAP2cpsC9eyv54KfvjB+yLl7I/Xsr++IFy6ouNKaX2mV5Y0oFsoUTEzpTSjqzrWAzsi5eyP17K/vgB++Kl7I+Xsj9+wL6Y4pSlJElSxgxkkiRJGTOQzc7tWRewiNgXL2V/vJT98QP2xUvZHy9lf/yAfYFryCRJkjLnCJkkSVLGDGSvICLeGxF7ImJfRNySdT0LISLWR8Q3IuKZiNgVEb9cbG+NiHsjYm/xsWXae24t9tGeiLguu+pLIyLyEfFoRHy5+Lyc+6I5Ij4fEbuLf0euKdf+iIhfKf4beSoiPhcR1eXUFxHxZxHRGRFPTWs77+8fEa+LiCeLr/1hRMRCf5f5cJb++J3iv5UnIuKLEdE87bWy649pr/1fEZEiom1a27Luj1lJKfkzww+QB54FtgBVwOPAtqzrWoDvvRp4bfG4Afg+sA34beCWYvstwH8qHm8r9k0B2Fzss3zW32Oe++RXgc8CXy4+L+e+uAP4F8XjKqC5HPsDWAvsB2qKz+8C/lk59QXwVuC1wFPT2s77+wMPAtcAAXwV+CdZf7d57I/3ABXF4/9U7v1RbF8PfI2pPUTbyqU/ZvPjCNnZXQ3sSyk9l1IaBe4Ers+4ppJLKR1LKT1SPO4DnmHqPz7XM/UfY4qPNxSPrwfuTCmNpJT2A/uY6rtlISLWAT8G/Om05nLti0amfsl+CiClNJpS6qZM+wOoAGoiogKoBY5SRn2RUvo2cPplzef1/SNiNdCYUro/Tf3X99PT3rOkzNQfKaWvp5TGi0//EVhXPC7L/ij6PeBfA9MXsC/7/pgNA9nZrQUOTXt+uNhWNiJiE3AV8ACwMqV0DKZCG9BRPG2599PvM/XLY3JaW7n2xRagC/jz4hTun0ZEHWXYHymlI8DvAgeBY0BPSunrlGFfvMz5fv+1xeOXty9H/5ypER4o0/6IiPcDR1JKj7/spbLsj5czkJ3dTPPUZXNJakTUA38NfDyl1PtKp87Qtiz6KSLeB3SmlB6e7VtmaFsWfVFUwdQUxB+llK4CBpialjqbZdsfxbVR1zM1vbIGqIuIn32lt8zQtiz6YpbO9v3Lol8i4t8C48BnXmia4bRl3R8RUQv8W+D/nunlGdqWdX/MxEB2doeZmut+wTqmpiSWvYioZCqMfSal9IVi84ni8DHFx85i+3LupzcB74+I55masn5nRPwl5dkXMPX9DqeUHig+/zxTAa0c++NdwP6UUldKaQz4AvAjlGdfTHe+3/8wP5jGm96+bETETcD7gJ8pTrtBefbHRUz9H5j/v727DbGiiuM4/v2pZSYhWUkvlFbIipIyykVLQzAjJKJCUFIyCnqgBwgiNQMpemEEQSBRQZCJ+iYfkgK1LBON8mFZd7fSyrTyhRAWZUm66r8X51x2druru7o5u97fB4Y7c2bOmTNnd2f/zDlzz858Tx0ONEi6nNpsj/9wQNa5bcAoSSMlnQ/MANaUXKf/XX6D5R3g24h4rbBrDTA7r88GPiikz5A0UNJIYBRpEGafFxHzImJ4RNSRfv6fRsQsarAtACLiAPCLpKtz0mTgG2qzPX4Gxkm6MP/NTCaNt6zFtijq1vXnbs1DksbldnygkKfPk3QnMAe4OyIOF3bVXHtERHNEDIuIunxP3U96gewANdgeVZX9VkFvXoCppLcM9wDzy67PWbrmCaRHwk1AY16mApcAG4Dv8+fQQp75uY12c46+AQNMou0ty5ptC2AMsD3/fqwGLq7V9gBeBHYBLcAS0htiNdMWwHLS+LlW0j/Xh0/n+oGbcxvuARaRv7C8ry2dtMcPpLFRlXvpm7XcHh327yO/ZVkL7dGVxd/Ub2ZmZlYyd1mamZmZlcwBmZmZmVnJHJCZmZmZlcwBmZmZmVnJHJCZmZmZlcwBmZmdVZL+yp91ku7v4bKf77D9RU+W39MkPShpUdn1MLPyOSAzs7LUAd0KyCT1P8Uh7QKyiLilm3XqU7rQHmbWRzggM7OyLAQmSmqU9Iyk/pJelbRNUpOkRwEkTZL0maRlQHNOWy1ph6SvJT2S0xYCg3J5S3Na5WmcctktkpolTS+UvVHS+5J2SVqavxG8nXzMK5K2SvpO0sSc3u4Jl6QPJU2qnDvn2SHpE0n1uZwf8yTLFSMkrZW0W9KCQlmz8vkaJb1VCb5yuS9J+goY31M/DDMr14CyK2BmNWsu8GxE3AWQA6s/ImKspIHAFknr87H1wOiI2Ju3H4qI3yQNArZJWhERcyU9GRFjqpzrPtIsAzcAl+Y8m/K+G4HrSHPkbSHNYbq5ShkDIqJe0lRgAWk+y5MZDGyMiDmSVgEvA1OAa4HFtE3FVg+MBg7nen1Emrh9OnBrRLRKegOYCbyXy22JiGqTNJtZH+WAzMx6izuA6yVNy9tDSHPaHSXNa7e3cOzTku7N6yPycQdPUvYEYHlEHCdNgP05MBb4M5e9H0BSI6krtVpAtjJ/7sjHnMpRYG1ebwaO5OCquUP+jyPiYD7/ylzXY8BNpAANYBBtE3UfB1Z04fxm1oc4IDOz3kLAUxGxrl1i6gL8u8P27cD4iDgsaSNwQRfK7syRwvpxOr8vHqlyzDHaD/0o1qM12uamO1HJHxEnJBXP0XH+usj1XRwR86rU458cWJrZOcRjyMysLIeAiwrb64DHJZ0HIOkqSYOr5BsC/J6DsWuAcYV9rZX8HWwCpudxapcBtwFbe+Aa9gFjJPWTNILU/dhdUyQNzd2v95C6TTcA0yQNA8j7r+iB+ppZL+UnZGZWlibgmKSdwLvA66SuvIY8sP5XUoDS0VrgMUlNwG7gy8K+t4EmSQ0RMbOQvoo0AH4n6QnUcxFxIAd0Z2ILsJfUJdkCNJxGGZuBJcCVwLKI2A4g6QVgvaR+QCvwBPDTGdbXzHoptT1RNzMzM7MyuMvSzMzMrGQOyMzMzMxK5oDMzMzMrGQOyMzMzMxK5oDMzMzMrGQOyMzMzMxK5oDMzMzMrGQOyMzMzMxK9i/gH24EpSyydgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.370939\n",
      "validation accuracy: 0.378000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print ('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print ('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 786.400707\n",
      "iteration 100 / 1500: loss 286.258821\n",
      "iteration 200 / 1500: loss 107.777258\n",
      "iteration 300 / 1500: loss 41.753613\n",
      "iteration 400 / 1500: loss 18.552789\n",
      "iteration 500 / 1500: loss 10.101731\n",
      "iteration 600 / 1500: loss 7.186688\n",
      "iteration 700 / 1500: loss 6.450671\n",
      "iteration 800 / 1500: loss 5.245222\n",
      "iteration 900 / 1500: loss 6.000549\n",
      "iteration 1000 / 1500: loss 5.303284\n",
      "iteration 1100 / 1500: loss 5.847904\n",
      "iteration 1200 / 1500: loss 5.258235\n",
      "iteration 1300 / 1500: loss 4.784595\n",
      "iteration 1400 / 1500: loss 5.232952\n",
      "iteration 0 / 1500: loss 1543.286313\n",
      "iteration 100 / 1500: loss 210.166003\n",
      "iteration 200 / 1500: loss 32.520457\n",
      "iteration 300 / 1500: loss 9.139302\n",
      "iteration 400 / 1500: loss 6.019860\n",
      "iteration 500 / 1500: loss 5.449686\n",
      "iteration 600 / 1500: loss 5.839032\n",
      "iteration 700 / 1500: loss 6.112388\n",
      "iteration 800 / 1500: loss 6.043405\n",
      "iteration 900 / 1500: loss 5.566851\n",
      "iteration 1000 / 1500: loss 6.049826\n",
      "iteration 1100 / 1500: loss 5.832751\n",
      "iteration 1200 / 1500: loss 5.909877\n",
      "iteration 1300 / 1500: loss 5.883086\n",
      "iteration 1400 / 1500: loss 5.818308\n",
      "iteration 0 / 1500: loss 793.828119\n",
      "iteration 100 / 1500: loss 425889576843572891389566010405821612032.000000\n",
      "iteration 200 / 1500: loss 70396136136191737574346615232275902125412439050665038007768415618683895808.000000\n",
      "iteration 300 / 1500: loss 11635917506207047453061728391815009826155615216869009930114516954912038588972688665301420062915592998900203520.000000\n",
      "iteration 400 / 1500: loss 1923323972629900474588446322424795861224887296380999025812780181391296828537223298519480032043887673068203793236067588200311114282092337878269952.000000\n",
      "iteration 500 / 1500: loss 317910048925628796391706874376228320543533259354096943613356178547001511766989277631502225137638982395999467088557579269030131889963139944756948586474869516643949593033071560491008.000000\n",
      "iteration 600 / 1500: loss 52547984970883364513838355354966542164137624403285448081822914708096458417721487203493047310419641825274106771213459475723135176002832386177243670978600479754249634413512823403710898751141212171296750620782324875264.000000\n",
      "iteration 700 / 1500: loss 8685761063017401199536063368840513158945727868895752558124334838400454277979827058557919431566149686982358680850358181132342629495635733367652889019908282814251541640231096068500727849571087305008248860930390074471145007823861101359612606618733117440.000000\n",
      "iteration 800 / 1500: loss 1435686740141064777902842227254352487924955855714222137283032115255317662321243991515151853101731909245098155065244897248622510653026492505501811951149108301058827143564048982083242076451958107001821980522963115277205303504808219485083068972614012782363567926471095130192956539245953024.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\cs231n_code\\assignment1\\cs231n\\classifiers\\linear_svm.py:87: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss+=0.5*reg*np.sum(W*W)\n",
      "E:\\anaconda\\lib\\site-packages\\numpy\\core\\fromnumeric.py:90: RuntimeWarning: overflow encountered in reduce\n",
      "  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n",
      "G:\\cs231n_code\\assignment1\\cs231n\\classifiers\\linear_svm.py:87: RuntimeWarning: overflow encountered in multiply\n",
      "  loss+=0.5*reg*np.sum(W*W)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 900 / 1500: loss inf\n",
      "iteration 1000 / 1500: loss inf\n",
      "iteration 1100 / 1500: loss inf\n",
      "iteration 1200 / 1500: loss inf\n",
      "iteration 1300 / 1500: loss inf\n",
      "iteration 1400 / 1500: loss inf\n",
      "iteration 0 / 1500: loss 1580.457274\n",
      "iteration 100 / 1500: loss 4288077798380708559926877810461734925436918169183943494581712365016932073144535208494170329795000701374650794344533983232000.000000\n",
      "iteration 200 / 1500: loss 11072888372095764070216862219747561479405734122357922298725613677841290166195433470531257836758088678947895821201944246070210891358374423961245576669421408447443058918022480112319377272755521725609071904026058135207557266062337787776815071232000.000000\n",
      "iteration 300 / 1500: loss inf\n",
      "iteration 400 / 1500: loss inf\n",
      "iteration 500 / 1500: loss inf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\anaconda\\lib\\site-packages\\numpy\\core\\_methods.py:38: RuntimeWarning: overflow encountered in reduce\n",
      "  return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n",
      "G:\\cs231n_code\\assignment1\\cs231n\\classifiers\\linear_svm.py:106: RuntimeWarning: overflow encountered in multiply\n",
      "  dW+=np.dot(X.T,counts)/N+reg*W;\n",
      "G:\\cs231n_code\\assignment1\\cs231n\\classifiers\\linear_svm.py:85: RuntimeWarning: invalid value encountered in greater\n",
      "  margin=(margin>0)*margin#只留下大于零的部分\n",
      "G:\\cs231n_code\\assignment1\\cs231n\\classifiers\\linear_svm.py:104: RuntimeWarning: invalid value encountered in greater\n",
      "  counts=(margin>0).astype(int)#强制类型转化为int N*C\n",
      "G:\\cs231n_code\\assignment1\\cs231n\\classifiers\\linear_classifier.py:67: RuntimeWarning: invalid value encountered in subtract\n",
      "  self.W-=grad*learning_rate #梯度下降\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 600 / 1500: loss nan\n",
      "iteration 700 / 1500: loss nan\n",
      "iteration 800 / 1500: loss nan\n",
      "iteration 900 / 1500: loss nan\n",
      "iteration 1000 / 1500: loss nan\n",
      "iteration 1100 / 1500: loss nan\n",
      "iteration 1200 / 1500: loss nan\n",
      "iteration 1300 / 1500: loss nan\n",
      "iteration 1400 / 1500: loss nan\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.371367 val accuracy: 0.393000\n",
      "lr 1.000000e-07 reg 1.000000e+05 train accuracy: 0.353408 val accuracy: 0.369000\n",
      "lr 5.000000e-05 reg 5.000000e+04 train accuracy: 0.063816 val accuracy: 0.054000\n",
      "lr 5.000000e-05 reg 1.000000e+05 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.393000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 5e-5]\n",
    "regularization_strengths = [5e4, 1e5,8e4]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "for i in range(len(learning_rates)):\n",
    "    for j in range(len(regularization_strengths)):\n",
    "        svm = LinearSVM()\n",
    "        svm.train(X_train, y_train, learning_rate=learning_rates[i], reg=regularization_strengths[j],\n",
    "                      num_iters=1500, verbose=True)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        Train_accuaracy=np.mean(y_train_pred==y_train)\n",
    "        Val_accuaracy=np.mean(y_val_pred==y_val)\n",
    "        results[(learning_rates[i],regularization_strengths[j])]=(Train_accuaracy,Val_accuaracy)\n",
    "        if Val_accuaracy>best_val:\n",
    "            best_val=Val_accuaracy\n",
    "            best_svm=svm\n",
    "pass\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print ('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print ('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.376000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print ('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "  plt.subplot(2, 5, i + 1)\n",
    "    \n",
    "  # Rescale the weights to be between 0 and 255\n",
    "  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "  plt.imshow(wimg.astype('uint8'))\n",
    "  plt.axis('off')\n",
    "  plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** *fill this in*"
   ]
  }
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